MétaCan
Menu
Back to cohort
Record W7098923390

Who is getting the public goods in India: Some evidence

2002· article· en· W7098923390 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemistry
TopicChemical synthesis and alkaloids
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)GirlRural districtRural areaPopulationFraction (chemistry)
DOInot available

Abstract

fetched live from OpenAlex

The way you grow up in India, it has long been known, depends on where you grow up. The average child growing up in Orissa in the 1980s was seven times more likely to die in infancy than his or her equivalent in Kerala. 2 His or her mother is four and half times more likely to die in giving birth if she were in Assam than she would be had she been in Kerala. 3 And if she happens to be a girl and born in Rajasthan in the 1980s, the likelihood of her being literate by the time she was 14 was about a quarter of what it would have been had she grown up in Kerala. 4 This is, as Dreze and Sen (1995), among others, have argued is entirely what we might have expected: In 1991, rural Kerala had 17 times as many hospital beds per head as Orissa and 10 times as many as Assam. The fraction of people in rural Orissa with access to medical facilities in their village in 1981 was less than 11 % compared to 96 % in Kerala. In 1991, 93 % of villages in Kerala had a middle school but the corresponding fraction in Orissa and Assam was less than 25 % and in UP it was less than 15%. What is less often emphasized but equally striking is the extent of variation within a single state: According to the 1991 census, less than 7 % of the villages in Vishakhapatnam district in Andhra Pradesh had middle schools and just over 46 % had some educational facility, as against 55 % and 100 % in Guntur. The district of Rangareddy had only 6 % of villages with primary health sub-centers as against almost 40 % in Anantapur. Less than 1 % of villages in Vishakhapatnam had tapped water compared to 59 % in West Godavari. Forty-eight percent of villages in Vishakhapatnam were using electrical power as against essentially 100 % in Krishna. Twenty percent of 1 I am grateful to Pranab Bardhan, Kaushik Basu and Maitreesh Ghatak for helpful comments. I also wish to thank, without implicating in any way, Lakshmi Iyer and Rohini Somanathan for their ongoing collaboration in the research that lies behind this paper. 2 Based on the 1991 census.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.040
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.012
Science and technology studies0.0020.007
Scholarly communication0.0070.005
Open science0.0030.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0240.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.248
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicChemical synthesis and alkaloidsFrench-language works237,207